Overview

Dataset statistics

Number of variables21
Number of observations45005
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 MiB
Average record size in memory168.0 B

Variable types

Categorical6
Numeric14
DateTime1

Alerts

Plate has a high cardinality: 1729 distinct values High cardinality
Deviation is highly correlated with Average_Deviation_Station_W5 and 3 other fieldsHigh correlation
Average_Deviation_Vehicle_W5 is highly correlated with Average_Deviation_Station_W5 and 4 other fieldsHigh correlation
Average_Deviation_Station_W5 is highly correlated with Deviation and 5 other fieldsHigh correlation
Average_Vehicle_Weekly is highly correlated with Average_Deviation_Vehicle_W5 and 3 other fieldsHigh correlation
Average_Station_Weekly is highly correlated with Deviation and 5 other fieldsHigh correlation
Average_Vehicle_Hourly is highly correlated with Deviation and 5 other fieldsHigh correlation
Average_Station_Hourly is highly correlated with Deviation and 4 other fieldsHigh correlation
Deviation is highly correlated with Average_Deviation_Station_W5 and 3 other fieldsHigh correlation
Average_Deviation_Vehicle_W5 is highly correlated with Average_Deviation_Station_W5 and 4 other fieldsHigh correlation
Average_Deviation_Station_W5 is highly correlated with Deviation and 5 other fieldsHigh correlation
Average_Vehicle_Weekly is highly correlated with Average_Deviation_Vehicle_W5 and 3 other fieldsHigh correlation
Average_Station_Weekly is highly correlated with Deviation and 5 other fieldsHigh correlation
Average_Vehicle_Hourly is highly correlated with Deviation and 5 other fieldsHigh correlation
Average_Station_Hourly is highly correlated with Deviation and 4 other fieldsHigh correlation
Deviation is highly correlated with Average_Vehicle_Hourly and 1 other fieldsHigh correlation
Average_Deviation_Station_W5 is highly correlated with Average_Station_WeeklyHigh correlation
Average_Vehicle_Weekly is highly correlated with Average_Station_WeeklyHigh correlation
Average_Station_Weekly is highly correlated with Average_Deviation_Station_W5 and 1 other fieldsHigh correlation
Average_Vehicle_Hourly is highly correlated with Deviation and 1 other fieldsHigh correlation
Average_Station_Hourly is highly correlated with Deviation and 1 other fieldsHigh correlation
Station is highly correlated with Vehicle_TypeHigh correlation
Vehicle_Type is highly correlated with Station and 1 other fieldsHigh correlation
Product is highly correlated with Vehicle_TypeHigh correlation
Vehicle_Type is highly correlated with Product and 1 other fieldsHigh correlation
CodProduct is highly correlated with Product and 1 other fieldsHigh correlation
Product is highly correlated with Vehicle_Type and 3 other fieldsHigh correlation
Tare is highly correlated with Qty_OrderedHigh correlation
Qty_Ordered is highly correlated with CodProduct and 2 other fieldsHigh correlation
Deviation is highly correlated with Block and 6 other fieldsHigh correlation
Station is highly correlated with Vehicle_Type and 1 other fieldsHigh correlation
Hour is highly correlated with InspectionHigh correlation
Month is highly correlated with Average_Vehicle_Weekly and 1 other fieldsHigh correlation
Inspection is highly correlated with HourHigh correlation
Block is highly correlated with Deviation and 2 other fieldsHigh correlation
Average_Deviation_Vehicle_W5 is highly correlated with Deviation and 5 other fieldsHigh correlation
Average_Deviation_Station_W5 is highly correlated with Deviation and 5 other fieldsHigh correlation
Average_Vehicle_Weekly is highly correlated with Deviation and 6 other fieldsHigh correlation
Average_Station_Weekly is highly correlated with Deviation and 6 other fieldsHigh correlation
Average_Vehicle_Hourly is highly correlated with Deviation and 6 other fieldsHigh correlation
Average_Station_Hourly is highly correlated with Deviation and 6 other fieldsHigh correlation
Tare_Date has unique values Unique
Deviation has 760 (1.7%) zeros Zeros
Hour has 575 (1.3%) zeros Zeros
Percentage_Blocks has 31626 (70.3%) zeros Zeros

Reproduction

Analysis started2022-05-26 16:13:52.710300
Analysis finished2022-05-26 16:14:40.603646
Duration47.89 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Vehicle_Type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
Z002
28038 
Z004
16967 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters180020
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZ004
2nd rowZ004
3rd rowZ004
4th rowZ004
5th rowZ004

Common Values

ValueCountFrequency (%)
Z00228038
62.3%
Z00416967
37.7%

Length

2022-05-26T17:14:40.749859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-26T17:14:40.918660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
z00228038
62.3%
z00416967
37.7%

Most occurring characters

ValueCountFrequency (%)
090010
50.0%
Z45005
25.0%
228038
 
15.6%
416967
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number135015
75.0%
Uppercase Letter45005
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
090010
66.7%
228038
 
20.8%
416967
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
Z45005
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common135015
75.0%
Latin45005
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
090010
66.7%
228038
 
20.8%
416967
 
12.6%
Latin
ValueCountFrequency (%)
Z45005
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII180020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
090010
50.0%
Z45005
25.0%
228038
 
15.6%
416967
 
9.4%

CodProduct
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.437773581
Minimum2
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2022-05-26T17:14:41.067187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q35
95-th percentile37
Maximum42
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.235186987
Coefficient of variation (CV)1.51444095
Kurtosis13.82027043
Mean5.437773581
Median Absolute Deviation (MAD)1
Skewness3.899142434
Sum244727
Variance67.8183047
MonotonicityNot monotonic
2022-05-26T17:14:41.249896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
314444
32.1%
514215
31.6%
212254
27.2%
421127
 
2.5%
6967
 
2.1%
40728
 
1.6%
11449
 
1.0%
37429
 
1.0%
7392
 
0.9%
ValueCountFrequency (%)
212254
27.2%
314444
32.1%
514215
31.6%
6967
 
2.1%
7392
 
0.9%
11449
 
1.0%
37429
 
1.0%
40728
 
1.6%
421127
 
2.5%
ValueCountFrequency (%)
421127
 
2.5%
40728
 
1.6%
37429
 
1.0%
11449
 
1.0%
7392
 
0.9%
6967
 
2.1%
514215
31.6%
314444
32.1%
212254
27.2%

Product
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
CIMENT II/AL 32,5 R PALETTE
14444 
CIMENT I 42,5 R SAC
14215 
CIMENT II/AL 32,5 R SAC
12254 
CIMENT I 42,5 R SR3 PALETTE
 
1127
CIMENT I 42,5 R PALETTE
 
967
Other values (4)
1998 

Length

Max length28
Median length27
Mean length23.08405733
Min length17

Characters and Unicode

Total characters1038898
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCIMENT II/AL 32,5 R PALETTE
2nd rowCIMENT II/AL 32,5 R PALETTE
3rd rowCIMENT II/AL 32,5 R PALETTE
4th rowCIMENT II/AL 32,5 R PALETTE
5th rowCIMENT II/AL 32,5 R PALETTE

Common Values

ValueCountFrequency (%)
CIMENT II/AL 32,5 R PALETTE14444
32.1%
CIMENT I 42,5 R SAC14215
31.6%
CIMENT II/AL 32,5 R SAC12254
27.2%
CIMENT I 42,5 R SR3 PALETTE1127
 
2.5%
CIMENT I 42,5 R PALETTE967
 
2.1%
CIMENT II/AL 42,5 N SAC728
 
1.6%
CHAUX CHA 10 SAC449
 
1.0%
CHAUX CHA 10 PALETTE429
 
1.0%
CIMENT I 42,5 R SR3 SAC392
 
0.9%

Length

2022-05-26T17:14:41.436830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-26T17:14:41.629888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
ciment44127
19.6%
r43399
19.2%
sac28038
12.4%
ii/al27426
12.2%
32,526698
11.8%
42,517429
 
7.7%
palette16967
 
7.5%
i16701
 
7.4%
sr31519
 
0.7%
chaux878
 
0.4%
Other values (3)2484
 
1.1%

Most occurring characters

ValueCountFrequency (%)
183450
17.7%
I115680
11.1%
E78061
 
7.5%
T78061
 
7.5%
A74187
 
7.1%
C73921
 
7.1%
R44918
 
4.3%
N44855
 
4.3%
L44393
 
4.3%
544127
 
4.2%
Other values (13)257245
24.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter648239
62.4%
Space Separator183450
 
17.7%
Decimal Number135656
 
13.1%
Other Punctuation71553
 
6.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I115680
17.8%
E78061
12.0%
T78061
12.0%
A74187
11.4%
C73921
11.4%
R44918
 
6.9%
N44855
 
6.9%
L44393
 
6.8%
M44127
 
6.8%
S29557
 
4.6%
Other values (4)20479
 
3.2%
Decimal Number
ValueCountFrequency (%)
544127
32.5%
244127
32.5%
328217
20.8%
417429
 
12.8%
1878
 
0.6%
0878
 
0.6%
Other Punctuation
ValueCountFrequency (%)
,44127
61.7%
/27426
38.3%
Space Separator
ValueCountFrequency (%)
183450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin648239
62.4%
Common390659
37.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
I115680
17.8%
E78061
12.0%
T78061
12.0%
A74187
11.4%
C73921
11.4%
R44918
 
6.9%
N44855
 
6.9%
L44393
 
6.8%
M44127
 
6.8%
S29557
 
4.6%
Other values (4)20479
 
3.2%
Common
ValueCountFrequency (%)
183450
47.0%
544127
 
11.3%
,44127
 
11.3%
244127
 
11.3%
328217
 
7.2%
/27426
 
7.0%
417429
 
4.5%
1878
 
0.2%
0878
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1038898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
183450
17.7%
I115680
11.1%
E78061
 
7.5%
T78061
 
7.5%
A74187
 
7.1%
C73921
 
7.1%
R44918
 
4.3%
N44855
 
4.3%
L44393
 
4.3%
544127
 
4.2%
Other values (13)257245
24.8%

Tare
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1509
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15574.57749
Minimum1560
Maximum50580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2022-05-26T17:14:41.821326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1560
5-th percentile8540
Q114620
median15140
Q315880
95-th percentile19574
Maximum50580
Range49020
Interquartile range (IQR)1260

Descriptive statistics

Standard deviation4056.332084
Coefficient of variation (CV)0.2604457223
Kurtosis12.58287861
Mean15574.57749
Median Absolute Deviation (MAD)620
Skewness2.290968597
Sum700933860
Variance16453829.97
MonotonicityNot monotonic
2022-05-26T17:14:42.151423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15100553
 
1.2%
14900527
 
1.2%
14800510
 
1.1%
15000499
 
1.1%
15200495
 
1.1%
15300489
 
1.1%
14960461
 
1.0%
14980459
 
1.0%
15240442
 
1.0%
15060439
 
1.0%
Other values (1499)40131
89.2%
ValueCountFrequency (%)
15601
 
< 0.1%
17602
 
< 0.1%
17801
 
< 0.1%
18001
 
< 0.1%
18601
 
< 0.1%
18801
 
< 0.1%
24802
 
< 0.1%
25009
 
< 0.1%
252023
0.1%
25408
 
< 0.1%
ValueCountFrequency (%)
505801
< 0.1%
493801
< 0.1%
483801
< 0.1%
477801
< 0.1%
474401
< 0.1%
473801
< 0.1%
471001
< 0.1%
470201
< 0.1%
469401
< 0.1%
468001
< 0.1%

Qty_Ordered
Real number (ℝ≥0)

HIGH CORRELATION

Distinct116
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28633.03411
Minimum500
Maximum105000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2022-05-26T17:14:42.352728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile13000
Q130000
median30000
Q330000
95-th percentile35000
Maximum105000
Range104500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8425.838137
Coefficient of variation (CV)0.294269832
Kurtosis19.26417593
Mean28633.03411
Median Absolute Deviation (MAD)0
Skewness2.043382457
Sum1288629700
Variance70994748.32
MonotonicityNot monotonic
2022-05-26T17:14:42.556228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000033285
74.0%
150002752
 
6.1%
350002661
 
5.9%
10000741
 
1.6%
25000725
 
1.6%
12000438
 
1.0%
20000421
 
0.9%
25500288
 
0.6%
5000288
 
0.6%
9000273
 
0.6%
Other values (106)3133
 
7.0%
ValueCountFrequency (%)
5001
 
< 0.1%
100010
 
< 0.1%
15007
 
< 0.1%
200016
 
< 0.1%
25008
 
< 0.1%
28003
 
< 0.1%
300069
0.2%
35005
 
< 0.1%
400038
0.1%
45007
 
< 0.1%
ValueCountFrequency (%)
1050001
 
< 0.1%
10000038
 
0.1%
950003
 
< 0.1%
9000082
0.2%
880002
 
< 0.1%
870002
 
< 0.1%
860004
 
< 0.1%
85000106
0.2%
840009
 
< 0.1%
830007
 
< 0.1%

Deviation
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1431
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6427949477
Minimum-3.08
Maximum2.904761905
Zeros760
Zeros (%)1.7%
Negative9685
Negative (%)21.5%
Memory size351.7 KiB
2022-05-26T17:14:42.738778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.08
5-th percentile-1.133333333
Q10.1
median0.7
Q31.285714286
95-th percentile2.222222222
Maximum2.904761905
Range5.984761905
Interquartile range (IQR)1.185714286

Descriptive statistics

Standard deviation0.9894550089
Coefficient of variation (CV)1.539301161
Kurtosis0.5441664618
Mean0.6427949477
Median Absolute Deviation (MAD)0.6
Skewness-0.4536032009
Sum28928.98662
Variance0.9790212146
MonotonicityNot monotonic
2022-05-26T17:14:42.939751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1860
 
1.9%
0.3333333333858
 
1.9%
0.6666666667834
 
1.9%
0.5807
 
1.8%
0760
 
1.7%
0.8333333333697
 
1.5%
0.6675
 
1.5%
0.8662
 
1.5%
0.4651
 
1.4%
0.1666666667645
 
1.4%
Other values (1421)37556
83.4%
ValueCountFrequency (%)
-3.081
 
< 0.1%
-3.0714285711
 
< 0.1%
-3.0666666678
 
< 0.1%
-3.0555555561
 
< 0.1%
-3.0357142861
 
< 0.1%
-3.0333333335
 
< 0.1%
-328
0.1%
-2.9666666674
 
< 0.1%
-2.961
 
< 0.1%
-2.9591836731
 
< 0.1%
ValueCountFrequency (%)
2.9047619051
 
< 0.1%
2.9032258061
 
< 0.1%
2.9019607841
 
< 0.1%
2.941
0.1%
2.8965517241
 
< 0.1%
2.8888888896
 
< 0.1%
2.8857142863
 
< 0.1%
2.881
 
< 0.1%
2.8753
 
< 0.1%
2.86666666750
0.1%

Station
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
Unknown
16260 
SPEED2
5848 
SPEED1
5424 
PAL
5009 
AUTOPAC
4998 
Other values (3)
7466 

Length

Max length7
Median length6
Mean length5.806665926
Min length3

Characters and Unicode

Total characters261329
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown16260
36.1%
SPEED25848
 
13.0%
SPEED15424
 
12.1%
PAL5009
 
11.1%
AUTOPAC4998
 
11.1%
PAL24048
 
9.0%
ENV21768
 
3.9%
ENV31650
 
3.7%

Length

2022-05-26T17:14:43.122155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-26T17:14:43.329847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown16260
36.1%
speed25848
 
13.0%
speed15424
 
12.1%
pal5009
 
11.1%
autopac4998
 
11.1%
pal24048
 
9.0%
env21768
 
3.9%
env31650
 
3.7%

Most occurring characters

ValueCountFrequency (%)
n48780
18.7%
E25962
9.9%
P25327
9.7%
U21258
8.1%
A19053
 
7.3%
k16260
 
6.2%
o16260
 
6.2%
w16260
 
6.2%
211664
 
4.5%
D11272
 
4.3%
Other values (9)49233
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter145031
55.5%
Lowercase Letter97560
37.3%
Decimal Number18738
 
7.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E25962
17.9%
P25327
17.5%
U21258
14.7%
A19053
13.1%
D11272
7.8%
S11272
7.8%
L9057
 
6.2%
T4998
 
3.4%
O4998
 
3.4%
C4998
 
3.4%
Other values (2)6836
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
n48780
50.0%
k16260
 
16.7%
o16260
 
16.7%
w16260
 
16.7%
Decimal Number
ValueCountFrequency (%)
211664
62.2%
15424
28.9%
31650
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Latin242591
92.8%
Common18738
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n48780
20.1%
E25962
10.7%
P25327
10.4%
U21258
8.8%
A19053
 
7.9%
k16260
 
6.7%
o16260
 
6.7%
w16260
 
6.7%
D11272
 
4.6%
S11272
 
4.6%
Other values (6)30887
12.7%
Common
ValueCountFrequency (%)
211664
62.2%
15424
28.9%
31650
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII261329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n48780
18.7%
E25962
9.9%
P25327
9.7%
U21258
8.1%
A19053
 
7.3%
k16260
 
6.2%
o16260
 
6.2%
w16260
 
6.2%
211664
 
4.5%
D11272
 
4.3%
Other values (9)49233
18.8%

Tare_Date
Date

UNIQUE

Distinct45005
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
Minimum2019-11-29 17:15:47.093000
Maximum2022-05-05 11:20:57.730000
2022-05-26T17:14:43.529590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:43.743819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Plate
Categorical

HIGH CARDINALITY

Distinct1729
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
117TU139
 
632
8277TU188
 
589
687TU166
 
566
6165TU183
 
489
6289TU143
 
463
Other values (1724)
42266 

Length

Max length10
Median length9
Mean length8.719031219
Min length3

Characters and Unicode

Total characters392400
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique603 ?
Unique (%)1.3%

Sample

1st row7952TU75
2nd row4350TU79
3rd row9836TU122
4th row6322TU130
5th row6505TU97

Common Values

ValueCountFrequency (%)
117TU139632
 
1.4%
8277TU188589
 
1.3%
687TU166566
 
1.3%
6165TU183489
 
1.1%
6289TU143463
 
1.0%
9788TU123443
 
1.0%
3133TU173437
 
1.0%
4857TU106422
 
0.9%
5567TU92409
 
0.9%
3904TU216409
 
0.9%
Other values (1719)40146
89.2%

Length

2022-05-26T17:14:43.941288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
117tu139633
 
1.4%
8277tu188589
 
1.3%
687tu166566
 
1.3%
6165tu183489
 
1.1%
6289tu143463
 
1.0%
9788tu123443
 
1.0%
3133tu173437
 
1.0%
4857tu106422
 
0.9%
5567tu92409
 
0.9%
3904tu216409
 
0.9%
Other values (1714)40145
89.2%

Most occurring characters

ValueCountFrequency (%)
156414
14.4%
T44219
11.3%
U44219
11.3%
233024
8.4%
829823
7.6%
728578
7.3%
328174
7.2%
927798
7.1%
626456
6.7%
525239
6.4%
Other values (6)48456
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number303902
77.4%
Uppercase Letter88488
 
22.6%
Lowercase Letter10
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
156414
18.6%
233024
10.9%
829823
9.8%
728578
9.4%
328174
9.3%
927798
9.1%
626456
8.7%
525239
8.3%
024217
8.0%
424179
8.0%
Uppercase Letter
ValueCountFrequency (%)
T44219
50.0%
U44219
50.0%
R25
 
< 0.1%
S25
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
t5
50.0%
u5
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common303902
77.4%
Latin88498
 
22.6%

Most frequent character per script

Common
ValueCountFrequency (%)
156414
18.6%
233024
10.9%
829823
9.8%
728578
9.4%
328174
9.3%
927798
9.1%
626456
8.7%
525239
8.3%
024217
8.0%
424179
8.0%
Latin
ValueCountFrequency (%)
T44219
50.0%
U44219
50.0%
R25
 
< 0.1%
S25
 
< 0.1%
t5
 
< 0.1%
u5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
156414
14.4%
T44219
11.3%
U44219
11.3%
233024
8.4%
829823
7.6%
728578
7.3%
328174
7.2%
927798
7.1%
626456
6.7%
525239
6.4%
Other values (6)48456
12.3%

Hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.87639151
Minimum0
Maximum23
Zeros575
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2022-05-26T17:14:44.108348image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.245169881
Coefficient of variation (CV)0.4416467642
Kurtosis-0.6176640195
Mean11.87639151
Median Absolute Deviation (MAD)4
Skewness0.1246842755
Sum534497
Variance27.51180708
MonotonicityNot monotonic
2022-05-26T17:14:44.289908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
113449
 
7.7%
103220
 
7.2%
93008
 
6.7%
142976
 
6.6%
62969
 
6.6%
152757
 
6.1%
82747
 
6.1%
52619
 
5.8%
72532
 
5.6%
132452
 
5.4%
Other values (14)16276
36.2%
ValueCountFrequency (%)
0575
 
1.3%
1406
 
0.9%
2325
 
0.7%
3463
 
1.0%
4403
 
0.9%
52619
5.8%
62969
6.6%
72532
5.6%
82747
6.1%
93008
6.7%
ValueCountFrequency (%)
23818
 
1.8%
221052
 
2.3%
211324
2.9%
20863
 
1.9%
191307
2.9%
181692
3.8%
172222
4.9%
162432
5.4%
152757
6.1%
142976
6.6%

Month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.569758916
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2022-05-26T17:14:44.458420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.603240235
Coefficient of variation (CV)0.5484585174
Kurtosis-1.346857439
Mean6.569758916
Median Absolute Deviation (MAD)3
Skewness0.007973125996
Sum295672
Variance12.98334019
MonotonicityNot monotonic
2022-05-26T17:14:44.612278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
35623
12.5%
124729
10.5%
104028
9.0%
113941
8.8%
63834
8.5%
23774
8.4%
93701
8.2%
13635
8.1%
43190
7.1%
73104
6.9%
Other values (2)5446
12.1%
ValueCountFrequency (%)
13635
8.1%
23774
8.4%
35623
12.5%
43190
7.1%
52376
5.3%
63834
8.5%
73104
6.9%
83070
6.8%
93701
8.2%
104028
9.0%
ValueCountFrequency (%)
124729
10.5%
113941
8.8%
104028
9.0%
93701
8.2%
83070
6.8%
73104
6.9%
63834
8.5%
52376
5.3%
43190
7.1%
35623
12.5%

Day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.76622597
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2022-05-26T17:14:44.781669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.921466114
Coefficient of variation (CV)0.565859333
Kurtosis-1.243294191
Mean15.76622597
Median Absolute Deviation (MAD)8
Skewness0.0289489114
Sum709559
Variance79.59255763
MonotonicityNot monotonic
2022-05-26T17:14:44.966575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
91682
 
3.7%
291678
 
3.7%
81673
 
3.7%
21624
 
3.6%
111624
 
3.6%
31595
 
3.5%
241591
 
3.5%
281564
 
3.5%
71558
 
3.5%
251533
 
3.4%
Other values (21)28883
64.2%
ValueCountFrequency (%)
11228
2.7%
21624
3.6%
31595
3.5%
41510
3.4%
51432
3.2%
61466
3.3%
71558
3.5%
81673
3.7%
91682
3.7%
101526
3.4%
ValueCountFrequency (%)
31866
1.9%
301495
3.3%
291678
3.7%
281564
3.5%
271342
3.0%
261499
3.3%
251533
3.4%
241591
3.5%
231502
3.3%
221336
3.0%

Inspection
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
0
33158 
1
11847 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033158
73.7%
111847
 
26.3%

Length

2022-05-26T17:14:45.256831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-26T17:14:45.416703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
033158
73.7%
111847
 
26.3%

Most occurring characters

ValueCountFrequency (%)
033158
73.7%
111847
 
26.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45005
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
033158
73.7%
111847
 
26.3%

Most occurring scripts

ValueCountFrequency (%)
Common45005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
033158
73.7%
111847
 
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII45005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
033158
73.7%
111847
 
26.3%

Block
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
0
41067 
1
 
3938

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45005
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
041067
91.2%
13938
 
8.8%

Length

2022-05-26T17:14:45.563868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-26T17:14:45.721576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
041067
91.2%
13938
 
8.8%

Most occurring characters

ValueCountFrequency (%)
041067
91.2%
13938
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45005
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
041067
91.2%
13938
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common45005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
041067
91.2%
13938
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII45005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
041067
91.2%
13938
 
8.8%

Average_Deviation_Vehicle_W5
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct32344
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6427321895
Minimum-2.273333333
Maximum2.7875
Zeros0
Zeros (%)0.0%
Negative7105
Negative (%)15.8%
Memory size351.7 KiB
2022-05-26T17:14:45.891516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2.273333333
5-th percentile-0.54
Q10.2428571429
median0.6761904762
Q31.086666667
95-th percentile1.69047619
Maximum2.7875
Range5.060833333
Interquartile range (IQR)0.8438095238

Descriptive statistics

Standard deviation0.6680779873
Coefficient of variation (CV)1.039434462
Kurtosis0.3671083222
Mean0.6427321895
Median Absolute Deviation (MAD)0.4228571429
Skewness-0.3439902595
Sum28926.16219
Variance0.4463281972
MonotonicityNot monotonic
2022-05-26T17:14:46.095218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.533333333317
 
< 0.1%
1.06666666715
 
< 0.1%
1.03333333315
 
< 0.1%
0.786666666714
 
< 0.1%
0.513
 
< 0.1%
1.26666666713
 
< 0.1%
0.566666666713
 
< 0.1%
1.03333333313
 
< 0.1%
1.21333333312
 
< 0.1%
1.01333333312
 
< 0.1%
Other values (32334)44868
99.7%
ValueCountFrequency (%)
-2.2733333331
< 0.1%
-2.2590476191
< 0.1%
-2.2523809521
< 0.1%
-2.211
< 0.1%
-2.1590476191
< 0.1%
-2.1186666671
< 0.1%
-2.1133333331
< 0.1%
-2.0533333331
< 0.1%
-21
< 0.1%
-1.9866666671
< 0.1%
ValueCountFrequency (%)
2.78751
< 0.1%
2.6733333331
< 0.1%
2.641
< 0.1%
2.6190476191
< 0.1%
2.6171428571
< 0.1%
2.6142857141
< 0.1%
2.6083333331
< 0.1%
2.61
< 0.1%
2.61
< 0.1%
2.598586791
< 0.1%

Average_Deviation_Station_W5
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29615
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6426083142
Minimum-2.71037037
Maximum2.7875
Zeros0
Zeros (%)0.0%
Negative7294
Negative (%)16.2%
Memory size351.7 KiB
2022-05-26T17:14:46.301039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2.71037037
5-th percentile-0.7
Q10.2533333333
median0.6933333333
Q31.113333333
95-th percentile1.76
Maximum2.7875
Range5.49787037
Interquartile range (IQR)0.86

Descriptive statistics

Standard deviation0.7293835214
Coefficient of variation (CV)1.135035924
Kurtosis0.9552737495
Mean0.6426083142
Median Absolute Deviation (MAD)0.4298305085
Skewness-0.5722648232
Sum28920.58718
Variance0.5320003213
MonotonicityNot monotonic
2022-05-26T17:14:46.495673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.419
 
< 0.1%
0.853333333319
 
< 0.1%
119
 
< 0.1%
0.706666666718
 
< 0.1%
0.366666666718
 
< 0.1%
0.833333333318
 
< 0.1%
1.09333333318
 
< 0.1%
0.573333333318
 
< 0.1%
1.0617
 
< 0.1%
0.6817
 
< 0.1%
Other values (29605)44824
99.6%
ValueCountFrequency (%)
-2.710370371
< 0.1%
-2.621
< 0.1%
-2.6133333331
< 0.1%
-2.6066666671
< 0.1%
-2.6037037041
< 0.1%
-2.5759139781
< 0.1%
-2.5590588241
< 0.1%
-2.5495833331
< 0.1%
-2.5492473121
< 0.1%
-2.5425806451
< 0.1%
ValueCountFrequency (%)
2.78751
< 0.1%
2.7861111111
< 0.1%
2.774572651
< 0.1%
2.7533333331
< 0.1%
2.7111111111
< 0.1%
2.7014957261
< 0.1%
2.6903846151
< 0.1%
2.6888888891
< 0.1%
2.6666666671
< 0.1%
2.6533333331
< 0.1%

Average_Vehicle_Weekly
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct246
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6428158874
Minimum-0.5533123918
Maximum1.782937883
Zeros0
Zeros (%)0.0%
Negative3537
Negative (%)7.9%
Memory size351.7 KiB
2022-05-26T17:14:46.692493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.5533123918
5-th percentile-0.2854489728
Q10.3804835896
median0.65737362
Q30.9401111367
95-th percentile1.439045263
Maximum1.782937883
Range2.336250275
Interquartile range (IQR)0.5596275472

Descriptive statistics

Standard deviation0.4429963055
Coefficient of variation (CV)0.6891495904
Kurtosis0.5581371008
Mean0.6428158874
Median Absolute Deviation (MAD)0.2768900304
Skewness-0.292749531
Sum28929.92901
Variance0.1962457267
MonotonicityNot monotonic
2022-05-26T17:14:46.897551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2563274769468
 
1.0%
0.360029522456
 
1.0%
0.4009192954443
 
1.0%
0.9401111367438
 
1.0%
0.915772554421
 
0.9%
0.4231653858389
 
0.9%
0.7915963831388
 
0.9%
-0.3740278066354
 
0.8%
0.1641094091344
 
0.8%
0.83060979340
 
0.8%
Other values (236)40964
91.0%
ValueCountFrequency (%)
-0.5533123918314
0.7%
-0.5308365733289
0.6%
-0.5030525334208
0.5%
-0.4323881149329
0.7%
-0.42423959117
 
0.3%
-0.4135966172284
0.6%
-0.3740278066354
0.8%
-0.3186466638320
0.7%
-0.2854489728219
0.5%
-0.1306056381139
 
0.3%
ValueCountFrequency (%)
1.782937883186
0.4%
1.744887348141
0.3%
1.727435991111
0.2%
1.677697487182
0.4%
1.67634408631
 
0.1%
1.636812173152
0.3%
1.62827973672
 
0.2%
1.568198273190
0.4%
1.560102554110
0.2%
1.522991434189
0.4%

Average_Station_Weekly
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct797
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6428258089
Minimum-1.7
Maximum2.522222222
Zeros2
Zeros (%)< 0.1%
Negative5051
Negative (%)11.2%
Memory size351.7 KiB
2022-05-26T17:14:47.094301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.7
5-th percentile-0.4322791344
Q10.3969914935
median0.6867207601
Q30.9607057669
95-th percentile1.432153966
Maximum2.522222222
Range4.222222222
Interquartile range (IQR)0.5637142734

Descriptive statistics

Standard deviation0.5496491156
Coefficient of variation (CV)0.8550514119
Kurtosis1.709212842
Mean0.6428258089
Median Absolute Deviation (MAD)0.2763354377
Skewness-0.7113110133
Sum28930.37553
Variance0.3021141502
MonotonicityNot monotonic
2022-05-26T17:14:47.289399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8321674964563
 
1.3%
0.8920709736466
 
1.0%
1.027712345466
 
1.0%
1.014274016407
 
0.9%
1.143236913405
 
0.9%
0.7747221488394
 
0.9%
0.8155479372370
 
0.8%
0.9373412701360
 
0.8%
0.3843046246358
 
0.8%
0.5225990787355
 
0.8%
Other values (787)40861
90.8%
ValueCountFrequency (%)
-1.71
 
< 0.1%
-1.664131513100
0.2%
-1.5888888893
 
< 0.1%
-1.5333333331
 
< 0.1%
-1.49614325111
 
< 0.1%
-1.3928571433
 
< 0.1%
-1.315880093117
0.3%
-1.2888888893
 
< 0.1%
-1.25132347673
0.2%
-1.09536444187
0.2%
ValueCountFrequency (%)
2.5222222223
 
< 0.1%
2.3333333331
 
< 0.1%
2.2666666671
 
< 0.1%
2.23958333332
0.1%
2.1857142866
 
< 0.1%
2.16957538541
0.1%
2.1333333332
 
< 0.1%
2.11176470617
< 0.1%
2.04769841315
 
< 0.1%
2.02333333310
 
< 0.1%

Average_Vehicle_Hourly
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7836
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6427469723
Minimum-3.055555556
Maximum2.9
Zeros191
Zeros (%)0.4%
Negative8053
Negative (%)17.9%
Memory size351.7 KiB
2022-05-26T17:14:47.478075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.055555556
5-th percentile-0.6916666667
Q10.2
median0.6833333333
Q31.14047619
95-th percentile1.85
Maximum2.9
Range5.955555556
Interquartile range (IQR)0.9404761905

Descriptive statistics

Standard deviation0.7733023656
Coefficient of variation (CV)1.203120977
Kurtosis0.8084644304
Mean0.6427469723
Median Absolute Deviation (MAD)0.4722222222
Skewness-0.380238754
Sum28926.82749
Variance0.5979965487
MonotonicityNot monotonic
2022-05-26T17:14:47.684899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5258
 
0.6%
1195
 
0.4%
0191
 
0.4%
0.6187
 
0.4%
0.8171
 
0.4%
1.5169
 
0.4%
0.4150
 
0.3%
1.2141
 
0.3%
0.7136
 
0.3%
0.9133
 
0.3%
Other values (7826)43274
96.2%
ValueCountFrequency (%)
-3.0555555561
 
< 0.1%
-3.0333333331
 
< 0.1%
-37
< 0.1%
-2.9591836731
 
< 0.1%
-2.9333333332
 
< 0.1%
-2.92
 
< 0.1%
-2.8666666672
 
< 0.1%
-2.8571428573
< 0.1%
-2.8333333331
 
< 0.1%
-2.83
< 0.1%
ValueCountFrequency (%)
2.915
< 0.1%
2.8857142861
 
< 0.1%
2.881
 
< 0.1%
2.868279572
 
< 0.1%
2.86666666712
< 0.1%
2.8611111111
 
< 0.1%
2.8571428572
 
< 0.1%
2.83333333310
< 0.1%
2.8166666672
 
< 0.1%
2.81251
 
< 0.1%

Average_Station_Hourly
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6280
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6427950923
Minimum-3.066666667
Maximum2.9
Zeros383
Zeros (%)0.9%
Negative8438
Negative (%)18.7%
Memory size351.7 KiB
2022-05-26T17:14:47.899565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.066666667
5-th percentile-0.9333333333
Q10.1818181818
median0.7
Q31.197222222
95-th percentile2
Maximum2.9
Range5.966666667
Interquartile range (IQR)1.01540404

Descriptive statistics

Standard deviation0.8704824323
Coefficient of variation (CV)1.354214497
Kurtosis0.9290329978
Mean0.6427950923
Median Absolute Deviation (MAD)0.5
Skewness-0.5269685384
Sum28928.99313
Variance0.7577396649
MonotonicityNot monotonic
2022-05-26T17:14:48.260684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5411
 
0.9%
0383
 
0.9%
0.6343
 
0.8%
1329
 
0.7%
0.3333333333317
 
0.7%
0.6666666667303
 
0.7%
0.8287
 
0.6%
1.2272
 
0.6%
0.4268
 
0.6%
0.8333333333266
 
0.6%
Other values (6270)41826
92.9%
ValueCountFrequency (%)
-3.0666666673
 
< 0.1%
-3.0555555561
 
< 0.1%
-3.0333333333
 
< 0.1%
-314
< 0.1%
-2.9666666673
 
< 0.1%
-2.9591836731
 
< 0.1%
-2.9333333332
 
< 0.1%
-2.9333333336
< 0.1%
-2.9017857141
 
< 0.1%
-2.93
 
< 0.1%
ValueCountFrequency (%)
2.923
0.1%
2.8888888892
 
< 0.1%
2.8857142862
 
< 0.1%
2.881
 
< 0.1%
2.8752
 
< 0.1%
2.868279572
 
< 0.1%
2.86666666720
< 0.1%
2.8611111111
 
< 0.1%
2.8571428573
 
< 0.1%
2.841
 
< 0.1%

Percentage_Blocks
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07729807799
Minimum0
Maximum1
Zeros31626
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2022-05-26T17:14:48.484400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile0.4
Maximum1
Range1
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.1343414601
Coefficient of variation (CV)1.737966371
Kurtosis3.663630504
Mean0.07729807799
Median Absolute Deviation (MAD)0
Skewness1.873662499
Sum3478.8
Variance0.01804762791
MonotonicityNot monotonic
2022-05-26T17:14:48.648232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
031626
70.3%
0.210075
 
22.4%
0.42677
 
5.9%
0.6550
 
1.2%
0.870
 
0.2%
17
 
< 0.1%
ValueCountFrequency (%)
031626
70.3%
0.210075
 
22.4%
0.42677
 
5.9%
0.6550
 
1.2%
0.870
 
0.2%
17
 
< 0.1%
ValueCountFrequency (%)
17
 
< 0.1%
0.870
 
0.2%
0.6550
 
1.2%
0.42677
 
5.9%
0.210075
 
22.4%
031626
70.3%

Interactions

2022-05-26T17:14:37.064995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:00.558884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:03.501670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:06.183577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:08.842020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:12.189622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:14.977080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:17.914905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:20.724307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:23.405068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:26.185200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:28.967597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:31.553640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:34.397800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:37.274014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:00.790552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:03.689167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:06.392339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:09.085952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:12.377978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:15.178102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:18.107981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:20.924532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:23.595098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:26.375400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:29.166773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:31.779841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:34.592777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:37.463269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:00.983248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:03.864542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:06.574717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:09.523828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:12.575630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:15.404499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:18.298307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:21.114874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:23.903543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:26.557925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:29.358100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:31.967556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:34.779959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:37.809006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:01.279456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:04.048465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:06.750419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:09.828564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:12.771305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:15.630948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:18.481098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:21.308361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:24.085962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:26.752457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:29.535324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:32.155849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:34.966184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:37.995181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:01.545745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:04.230610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:06.932187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:10.106073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:12.957819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:15.827957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:18.667088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:21.503576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:24.281538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:26.941504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:29.716636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:32.353279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:35.156682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:38.183994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:01.751490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:04.421740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:07.105728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:10.350652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:13.143183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:16.013503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:18.852709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:21.703784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:24.466573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:27.130666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:29.896284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:32.537220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:35.345386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:38.365489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:01.966753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:04.596250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:07.308214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:10.575614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:13.328206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:16.193938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:19.061117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:21.879407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:24.642106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:27.327039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:30.072618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:32.714771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:35.525960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:38.556013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:02.157873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:04.884600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:07.501990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:10.773177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:13.533175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:16.386986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:19.379751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:22.064116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:24.835638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:27.511283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:30.271998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:32.904094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:35.721291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:38.736384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:02.370322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:05.069697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:07.688333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:10.963336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:13.720299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:16.604375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:19.576627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:22.262934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:25.019090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:27.716725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:30.460457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:33.234517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:35.910884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:38.920614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:02.569873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:05.250775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:07.886733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:11.156810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:13.912918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:16.803404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:19.758597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:22.450634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:25.225839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:27.903880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:30.631237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:33.418474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:36.104744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:39.110736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:02.752130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:05.446244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:08.072953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:11.349707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:14.094934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:17.013539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:19.943895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:22.641980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:25.414036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:28.083033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:30.803988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:33.605789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:36.311375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:39.298794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:02.928968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:05.632357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:08.261417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:11.549206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:14.282404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:17.230408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:20.139336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:22.828223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:25.598426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:28.276068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:30.979753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:33.790858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:36.495831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:39.488524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:03.121613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:05.815682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:08.467140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:11.744923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:14.612679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:17.465108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:20.337382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:23.011094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:25.790178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:28.594029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:31.171188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:34.018869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:36.692583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:39.679658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:03.304489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:05.997279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:08.655656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:11.969456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:14.792737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:17.682526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:20.532673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:23.206447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:25.984614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:28.778534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:31.360189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:34.208901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-26T17:14:36.879313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-26T17:14:48.826705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-26T17:14:49.096135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-26T17:14:49.365969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-26T17:14:49.607397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-26T17:14:49.819310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-26T17:14:39.985236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-26T17:14:40.395613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Vehicle_TypeCodProductProductTareQty_OrderedDeviationStationTare_DatePlateHourMonthDayInspectionBlockAverage_Deviation_Vehicle_W5Average_Deviation_Station_W5Average_Vehicle_WeeklyAverage_Station_WeeklyAverage_Vehicle_HourlyAverage_Station_HourlyPercentage_Blocks
0Z0043CIMENT II/AL 32,5 R PALETTE1466030000.00.200000Unknown2019-11-29 17:15:47.0937952TU75171129000.9266671.0433330.8415990.8932750.9920001.0217460.0
1Z0043CIMENT II/AL 32,5 R PALETTE1474025000.01.160000Unknown2019-11-29 17:18:52.2104350TU79171129000.7333330.8900000.8415990.8932750.9920001.0217460.0
2Z0043CIMENT II/AL 32,5 R PALETTE1588030000.01.433333Unknown2019-11-29 17:29:35.0609836TU122171129000.9120000.8686670.8415990.8932750.9920001.0217460.0
3Z0043CIMENT II/AL 32,5 R PALETTE1462030000.00.400000Unknown2019-11-29 17:33:10.1276322TU130171129001.0053330.9020000.8415990.8932750.9920001.0217460.0
4Z0043CIMENT II/AL 32,5 R PALETTE1538030000.01.766667Unknown2019-11-29 17:48:10.5306505TU97171129000.8320000.9253330.8415990.8932750.9920001.0217460.0
5Z0025CIMENT I 42,5 R SAC1610035000.01.285714Unknown2019-11-29 17:50:01.9072156TU185171129000.9966671.2386670.7556640.8932751.0589291.0217460.0
6Z0025CIMENT I 42,5 R SAC1508030000.00.766667Unknown2019-11-29 17:59:14.1675809TU158171129001.1404761.2638100.7556640.8932751.0589291.0217460.0
7Z0025CIMENT I 42,5 R SAC1464030000.01.300000Unknown2019-11-29 18:02:25.2872189TU151181129101.1004761.1304760.7556640.8932750.7083330.8863950.0
8Z0043CIMENT II/AL 32,5 R PALETTE1512030000.00.566667Unknown2019-11-29 18:06:43.7302872TU107181129100.9920001.3104760.8415990.8932751.1238100.8863950.0
9Z0043CIMENT II/AL 32,5 R PALETTE1514035000.00.971429Unknown2019-11-29 18:21:42.1272552TU154181129101.0653331.1371430.8415990.8932751.1238100.8863950.0

Last rows

Vehicle_TypeCodProductProductTareQty_OrderedDeviationStationTare_DatePlateHourMonthDayInspectionBlockAverage_Deviation_Vehicle_W5Average_Deviation_Station_W5Average_Vehicle_WeeklyAverage_Station_WeeklyAverage_Vehicle_HourlyAverage_Station_HourlyPercentage_Blocks
44995Z0022CIMENT II/AL 32,5 R SAC725010000.01.000000SPEED22022-05-05 08:07:32.6931338TU21985500-0.1857140.7000000.0776360.7638890.0238101.0000000.2
44996Z0022CIMENT II/AL 32,5 R SAC1500030000.00.666667AUTOPAC2022-05-05 09:32:17.3103513TU8995500-0.0523810.2666670.0776360.3666670.6666670.5833330.0
44997Z0022CIMENT II/AL 32,5 R SAC1565030000.00.500000AUTOPAC2022-05-05 09:36:21.1278170TU152955000.1809520.4666670.0776360.3666670.6666670.5833330.0
44998Z0043CIMENT II/AL 32,5 R PALETTE29505000.01.000000PAL22022-05-05 09:42:18.7731718TU112955000.2066670.2066670.5625990.4643991.0000001.0000000.0
44999Z0022CIMENT II/AL 32,5 R SAC1525030000.00.833333SPEED22022-05-05 09:42:57.557319TU113955000.2476190.8000000.0776360.7638890.6666670.8333330.0
45000Z00442CIMENT I 42,5 R SR3 PALETTE745010000.01.500000PAL2022-05-05 10:04:47.5977891TU1831055000.8733330.3888890.5625991.2500001.5000001.5000000.0
45001Z0025CIMENT I 42,5 R SAC1480030000.0-0.166667SPEED12022-05-05 10:09:35.8905944TU1371055000.500000-0.5285710.077636-0.578297-0.444444-0.4444440.2
45002Z0025CIMENT I 42,5 R SAC1480030000.0-0.666667SPEED12022-05-05 10:29:36.163834TU1821055000.566667-0.4619050.077636-0.578297-0.444444-0.4444440.0
45003Z0025CIMENT I 42,5 R SAC1560030000.0-0.500000SPEED12022-05-05 10:53:35.4637472TU2031055000.233333-0.4523810.077636-0.578297-0.444444-0.4444440.2
45004Z0043CIMENT II/AL 32,5 R PALETTE1425015000.01.000000PAL2022-05-05 11:20:57.7309400TU1571155001.1333330.7555560.5625991.2500001.0000001.0000000.0